Climate models have become an important tool in understanding as well as projecting future climate conditions. They thus become very critical to decision-making processes, especially in mitigation and adaptation strategies. This paper provides a general overview of methodologies that are applied in climate modeling, its validation, and uncertainty mapping, notably their strengths, limitations, and challenges. The paper takes the reader through all the intricacies associated with the modeling of climate systems: global circulation models, regional climate models, and statistical downscaling. Much emphasis is given to the issue of model choice depending on the research objectives and spatial scales. Thorough model validation is considered, emphasizing that observational data forms the core and good quality and availability are issues. Climate modeling should incorporate uncertainty quantification as a core activity. This paper debates estimation and representation techniques of model uncertainties applied, such as probabilistic approaches, sensitivity analysis, and ensemble modeling. Introduce an idea called here uncertainty mapping, as a means of communicating the climate-related risks. This paper sets this tone by asserting that uncertainties in climate modeling have to be communicated as clearly as possible to decision-makers. No matter how intrinsically complex and limited they are, climate models represent, by themselves, very valuable means for the task of estimating the probable futures of climates. This quest ranges from enhancing model accuracy to ever-lowering estimates of uncertainties, all impelled by new advances made in computational powers and deeper insights into the climate systems.